A
Atefeh Zeinoddini
Researcher at Mayo Clinic
Publications - 4
Citations - 249
Atefeh Zeinoddini is an academic researcher from Mayo Clinic. The author has contributed to research in topics: Deep learning & Workflow. The author has an hindex of 3, co-authored 4 publications receiving 95 citations.
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Journal ArticleDOI
A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow
Zeynettin Akkus,Jason Cai,Arunnit Boonrod,Atefeh Zeinoddini,Alexander D. Weston,Kenneth A. Philbrick,Bradley J. Erickson +6 more
TL;DR: Current DL approaches and research directions in rapidly advancing ultrasound technology are reviewed and the outlook on future directions and trends for DL techniques to further improve diagnosis, reduce health care cost, and optimize ultrasound clinical workflow is presented.
Journal ArticleDOI
RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning
Kenneth A. Philbrick,Alexander D. Weston,Zeynettin Akkus,Timothy L. Kline,Panagiotis Korfiatis,Tomas Sakinis,Tomas Sakinis,Petro M. Kostandy,Arunnit Boonrod,Arunnit Boonrod,Atefeh Zeinoddini,Naoki Takahashi,Bradley J. Erickson +12 more
TL;DR: RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers.
Journal ArticleDOI
Complete abdomen and pelvis segmentation using U-net variant architecture.
Alexander D. Weston,Panagiotis Korfiatis,Kenneth A. Philbrick,Gian Marco Conte,Petro M. Kostandy,Thomas Sakinis,Atefeh Zeinoddini,Arunnit Boonrod,Michael R. Moynagh,Naoki Takahashi,Bradley J. Erickson +10 more
TL;DR: Fully automated deep-learning based segmentation of CT abdomen has the potential to improve both the speed and accuracy of radiotherapy dose prediction for organs-at-risk.
Journal ArticleDOI
Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning
Jason Cai,Zeynettin Akkus,Kenneth A. Philbrick,Arunnit Boonrod,Safa Hoodeshenas,Alexander D. Weston,Pouria Rouzrokh,Gian Marco Conte,Atefeh Zeinoddini,David C Vogelsang,Qiao Huang,Bradley J. Erickson +11 more
TL;DR: Automated segmentation of CT neuroanatomy is feasible with a high degree of accuracy and the model remained robust on external CT scans and scans demonstrating ventricular enlargement.